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1. Ensemble machine learning of factors influencing COVID-19 across US counties

   https://doi.org/10.1038/s41598-021-90827-x  

   McCoy, David; Mgbara, Whitney; Horvitz, Nir; Getz, Wayne M.; Hubbard, Alan (December 2021, Scientific Reports)

   Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) the causal agent for COVID-19, is a communicable disease spread through close contact. It is known to disproportionately impact certain communities due to both biological susceptibility and inequitable exposure. In this study, we investigate the most important health, social, and environmental factors impacting the early phases (before July, 2020) of per capita COVID-19 transmission and per capita all-cause mortality in US counties. We aggregate county-level physical and mental health, environmental pollution, access to health care, demographic characteristics, vulnerable population scores, and other epidemiological data to create a large feature set to analyze per capita COVID-19 outcomes. Because of the high-dimensionality, multicollinearity, and unknown interactions of the data, we use ensemble machine learning and marginal prediction methods to identify the most salient factors associated with several COVID-19 outbreak measure. Our variable importance results show that measures of ethnicity, public transportation and preventable diseases are the strongest predictors for both per capita COVID-19 incidence and mortality. Specifically, the CDC measures for minority populations, CDC measures for limited English, and proportion of Black- and/or African-American individuals in a county were the most important features for per capita COVID-19 cases within a month after the pandemic started in a county and also at the latest date examined. For per capita all-cause mortality at day 100 and total to date, we find that public transportation use and proportion of Black- and/or African-American individuals in a county are the strongest predictors. The methods predict that, keeping all other factors fixed, a 10% increase in public transportation use, all other factors remaining fixed at the observed values, is associated with increases mortality at day 100 of 2012 individuals (95% CI [1972, 2356]) and likewise a 10% increase in the proportion of Black- and/or African-American individuals in a county is associated with increases total deaths at end of study of 2067 (95% CI [1189, 2654]). Using data until the end of study, the same metric suggests ethnicity has double the association as the next most important factors, which are location, disease prevalence, and transit factors. Our findings shed light on societal patterns that have been reported and experienced in the U.S. by using robust methods to understand the features most responsible for transmission and sectors of society most vulnerable to infection and mortality. In particular, our results provide evidence of the disproportionate impact of the COVID-19 pandemic on minority populations. Our results suggest that mitigation measures, including how vaccines are distributed, could have the greatest impact if they are given with priority to the highest risk communities. 

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2. Severe Acute Respiratory Syndrome Coronavirus 2 Delta Vaccine Breakthrough Transmissibility in Alachua County, Florida

   https://doi.org/10.1093/cid/ciac197  

   Rife Magalis, Brittany; Rich, Shannan; Tagliamonte, Massimiliano S.; Mavian, Carla; Cash, Melanie N.; Riva, Alberto; Marini, Simone; Amador, David Moraga; Zhang, Yanping; Shapiro, Jerne; et al (March 2022, Clinical Infectious Diseases)

   Abstract BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant has caused a dramatic resurgence in infections in the United Sates, raising questions regarding potential transmissibility among vaccinated individuals. MethodsBetween October 2020 and July 2021, we sequenced 4439 SARS-CoV-2 full genomes, 23% of all known infections in Alachua County, Florida, including 109 vaccine breakthrough cases. Univariate and multivariate regression analyses were conducted to evaluate associations between viral RNA burden and patient characteristics. Contact tracing and phylogenetic analysis were used to investigate direct transmissions involving vaccinated individuals. ResultsThe majority of breakthrough sequences with lineage assignment were classified as Delta variants (74.6%) and occurred, on average, about 3 months (104 ± 57.5 days) after full vaccination, at the same time (June-July 2021) of Delta variant exponential spread within the county. Six Delta variant transmission pairs between fully vaccinated individuals were identified through contact tracing, 3 of which were confirmed by phylogenetic analysis. Delta breakthroughs exhibited broad viral RNA copy number values during acute infection (interquartile range, 1.2-8.64 Log copies/mL), on average 38% lower than matched unvaccinated patients (3.29-10.81 Log copies/mL, P < .00001). Nevertheless, 49% to 50% of all breakthroughs, and 56% to 60% of Delta-infected breakthroughs exhibited viral RNA levels above the transmissibility threshold (4 Log copies/mL) irrespective of time after vaccination. ConclusionsDelta infection transmissibility and general viral RNA quantification patterns in vaccinated individuals suggest limited levels of sterilizing immunity that need to be considered by public health policies. In particular, ongoing evaluation of vaccine boosters should specifically address whether extra vaccine doses curb breakthrough contribution to epidemic spread. 

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3. Telehealth Challenges for California Rural Hospitals in Reaching Latino Populations During COVID-19

   https://doi.org/10.2196/41562  

   Mauricio, Noemi; Newhart, Glen; Herber, Steven; Ahumada-Newhart, Veronica (January 2023, Journal of Medical Internet Research (JMIR), Iproceedings)

   BackgroundRural and remote communities were especially vulnerable to the COVID-19 pandemic due to the availability and capacity of rural health services. Research has found that key issues surrounded (1) the lack of staff, (2) the need for coordinated health services, and (3) operational and facility issues. Similarly, research also confirms that irrespective of hospital capacity issues existing during crisis, compared to urban communities, rural communities typically face poorer access to health services. Telehealth programs have long held promise for addressing health disparities perpetuated by inadequate health care access. In response to the current COVID-19 pandemic, Adventist Health Saint Helena Hospital, a rural hospital in northern California, urgently worked to expand telehealth services. However, as Adventist Health Saint Helena Hospital is the longest-serving rural hospital in the state of California, administrators were also able to draw on experiences from the pandemic of 1918/1919. Understanding their historically rural and heavily Latino populations, their telehealth approach was coupled with cultural approaches for prioritizing socially responsive and equitable access to health services. ObjectiveThis study aimed to present one rural community’s holistic sociotechnical response to COVID-19 in redesigning their health care delivery approach. Redesign efforts included the expansion of digital health services coupled with county-wide collaborations for nondigital mobile health centers, testing, and vaccination clinics to meet the needs of those with limited digital access and language barriers. MethodsWe present data on telehealth services for maintaining critical care services and a framework on the feasibility of private-public partnerships to address COVID-19 challenges. ResultsIn this paper, we provide a critical review of how a rural hospital adapted its health care approach to incorporate telehealth services and distance services to meet the needs of a diverse population. ConclusionsThis paper contributes empirical data on how rural communities can use telehealth technologies and community partnerships for a holistic community approach to meet health needs during a natural disaster. Conflicts of InterestNone declared. 

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4. Investigating associations between COVID-19 mortality and population-level health and socioeconomic indicators in the United States: A modeling study

   https://doi.org/10.1371/journal.pmed.1003693  

   Kandula, Sasikiran; Shaman, Jeffrey (July 2021, PLOS Medicine)

   Turner, Richard (Ed.)

   Background With the availability of multiple Coronavirus Disease 2019 (COVID-19) vaccines and the predicted shortages in supply for the near future, it is necessary to allocate vaccines in a manner that minimizes severe outcomes, particularly deaths. To date, vaccination strategies in the United States have focused on individual characteristics such as age and occupation. Here, we assess the utility of population-level health and socioeconomic indicators as additional criteria for geographical allocation of vaccines. Methods and findings County-level estimates of 14 indicators associated with COVID-19 mortality were extracted from public data sources. Effect estimates of the individual indicators were calculated with univariate models. Presence of spatial autocorrelation was established using Moran’s I statistic. Spatial simultaneous autoregressive (SAR) models that account for spatial autocorrelation in response and predictors were used to assess (i) the proportion of variance in county-level COVID-19 mortality that can explained by identified health/socioeconomic indicators (R 2 ); and (ii) effect estimates of each predictor. Adjusting for case rates, the selected indicators individually explain 24%–29% of the variability in mortality. Prevalence of chronic kidney disease and proportion of population residing in nursing homes have the highest R 2 . Mortality is estimated to increase by 43 per thousand residents (95% CI: 37–49; p < 0.001) with a 1% increase in the prevalence of chronic kidney disease and by 39 deaths per thousand (95% CI: 34–44; p < 0.001) with 1% increase in population living in nursing homes. SAR models using multiple health/socioeconomic indicators explain 43% of the variability in COVID-19 mortality in US counties, adjusting for case rates. R 2 was found to be not sensitive to the choice of SAR model form. Study limitations include the use of mortality rates that are not age standardized, a spatial adjacency matrix that does not capture human flows among counties, and insufficient accounting for interaction among predictors. Conclusions Significant spatial autocorrelation exists in COVID-19 mortality in the US, and population health/socioeconomic indicators account for a considerable variability in county-level mortality. In the context of vaccine rollout in the US and globally, national and subnational estimates of burden of disease could inform optimal geographical allocation of vaccines. 

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5. Associations between rurality and regional differences in sociodemographic factors and the 1918–20 influenza and 2020–21 COVID-19 pandemics in Missouri counties: An ecological study

   https://doi.org/10.1371/journal.pone.0290294  

   Sattenspiel, Lisa; Orbann, Carolyn; Bogan, Aaron; Ramirez, Hailey; Pirrone, Sean; Dahal, Sushma; McElroy, Jane A.; Wikle, Christopher K. (August 2023, PLOS ONE)

   Acharya, Binod (Ed.)

   This study compares pandemic experiences of Missouri’s 115 counties based on rurality and sociodemographic characteristics during the 1918–20 influenza and 2020–21 COVID-19 pandemics. The state’s counties and overall population distribution have remained relatively stable over the last century, which enables identification of long-lasting pandemic attributes. Sociodemographic data available at the county level for both time periods were taken from U.S. census data and used to create clusters of similar counties. Counties were also grouped by rural status (RSU), including fully (100%) rural, semirural (1–49% living in urban areas), and urban (>50% of the population living in urban areas). Deaths from 1918 through 1920 were collated from the Missouri Digital Heritage database and COVID-19 cases and deaths were downloaded from the Missouri COVID-19 dashboard. Results from sociodemographic analyses indicate that, during both time periods, average farm value, proportion White, and literacy were the most important determinants of sociodemographic clusters. Furthermore, the Urban/Central and Southeastern regions experienced higher mortality during both pandemics than did the North and South. Analyses comparing county groups by rurality indicated that throughout the 1918–20 influenza pandemic, urban counties had the highest and rural had the lowest mortality rates. Early in the 2020–21 COVID-19 pandemic, urban counties saw the most extensive epidemic spread and highest mortality, but as the epidemic progressed, cumulative mortality became highest in semirural counties. Additional results highlight the greater effects both pandemics had on county groups with lower rates of education and a lower proportion of Whites in the population. This was especially true for the far southeastern counties of Missouri (“the Bootheel”) during the COVID-19 pandemic. These results indicate that rural-urban and socioeconomic differences in health outcomes are long-standing problems that continue to be of significant importance, even though the overall quality of health care is substantially better in the 21 st century. 

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